The present invention relates to the electrical, electronic, and computer arts, and more specifically, to use of computers for automated diagnosis of medical image data.
Skin lesion segmentation is the first and a key step of computer-aided skin lesion diagnosis (CAD) and has significant implications for diagnosis system of melanoma. While the task of segmenting skin lesion is important, it is particularly challenging due to high variability of the lesion shape, presence of artefacts (e.g. hair and fiducial markers) and/or when there is a large color distribution for the skin lesion area. To address these difficulties in the segmentation task, several techniques have been proposed. Regarding classical algorithms, there are three main groups that include threshold-based, region growing, and active-contour-based methods.
Recently, deep learning based segmentation approaches have been proposed, which aim to predict pixel-wise labelling of the images. These approaches demonstrate superior performance over classical methods in terms of accuracy in segmentation task. More recently, different post-processing techniques such as Markov Random Field (MRFs) have been presented to solve these problems. Further, feature maps of the classification network have been used with an independent Conditional Random Field (CRF) post-processing technique to do segmentation.
Some other recent methods consider additional inference techniques such as region proposal (e.g. super-pixels). For example, there is a segmentation method, where the raw input image is transformed through a multi scale convolutional network, which produces a set of feature maps. The feature maps of all scales are concatenated, then the coarser-scale maps are upsampled to match the resolution of the finest image scale map. In parallel, a single segmentation technique using super-pixels is devised to exploit the natural contours of the image. Finally a supervised classifier is used to classify each super-pixel by computing the average class distribution of the dense features within the super-pixels.
Alternatively, the object detection is formulated as a multi-class super-pixel labelling problem. An energy minimization algorithm is implemented with several terms such as data cost, smooth term and label cost. The data cost is learned through a convolutional neural network. Following that, the smooth term and label cost term are used to obtain the final labelling of the super-pixels. The parameters in the labelling model are learned through a structural SVM. The super-pixel labels and pixelwise labels are combined in a supervised way to obtain a final segmentation outcome.